Seminar on stochastic PDEs: Lorick Huang (INSA-Toulouse) -- Portfolio optimisation

Date: Tuesday 8 Apr, 11am – 12pm 

Location: UNSW, Red Centre, Room 3085 and online (Zoom link:
https://unsw.zoom.us/j/89982624154?from=addon Passcode: 462350) 

Abstract: This article studies and solves the problem of optimal portfolio allocation
with CV@R penalty when dealing with imperfectly simulated financial assets.  We use a
Stochastic biased Mirror Descent to find optimal resource allocation for a portfolio
whose underlying assets cannot be generated exactly and may only be approximated with a
numerical scheme that satisfies suitable error bounds, under a risk management
constraint.  We establish almost sure asymptotic properties as well as the rate of
convergence for the averaged algorithm.  We then focus on the optimal tuning of the
overall procedure to obtain an optimized numerical cost.  Our results are then
illustrated numerically on simulated as well as real data sets.